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Sparse approximation is key to many signal processing, image processing, and machine learning applications. If multiple signals maintain some degree of dependency, for example, the support sets are statistically related, then it will generally be advantageous to jointly estimate the sparse representation vectors from the measurement vectors as opposed to solving for each signal individually. In this...
In this paper, a novel power spectrum density (PSD) estimation approach is proposed for accurate and efficient wideband spectrum sensing in Cognitive Radio (CR) systems. Based on the observed signal from a wideband receiver, the goal of determining the fluctuation-free signal PSD is formulated as a constrained Bayesian estimation problem, subject to spectral variation constraints between neighboring...
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